The present invention relates to ultrasonic imaging and, in particular, to an improved method and apparatus for calculating material displacement used to produce elasticity images including local strain, modulus and Poison' ratio images.
Ultrasonic elasticity imaging produces an image showing the elasticity of the material being measured. When used in medicine, elasticity imaging is analogous to palpation by a physician, that is, the pressing of tissue by the physician to feel differences in elasticity of underlying structures.
In a common form of elasticity imaging, two separate ultrasonic images are obtained, the first image with tissue in an undeformed state relative to the second image (“initial, pre-deformation”) and the second image with the tissue in a deformed state (“post deformation”). The two images are analyzed to deduce the amount of displacement of the tissue at corresponding areas within the images. One realization of tissue elasticity information is the local strain, i.e. the gradient in the displacement computed at many points over the image provides an indication of the tissue elasticity at those points. The general principles of elasticity imaging and techniques for determining displacement of the tissue between two ultrasonic images are described in detail in U.S. Pat. No. 6,508,768, hereby incorporated by reference.
An important aspect of processing the pre-deformation and post-deformation ultrasonic images to deduce the displacement of tissue elements is identifying corresponding points in the two images. This is normally accomplished by identifying each point in the pre-deformation image and establishing a region of points (kernel) surrounding that identified point. This kernel is then moved within a search window within the post-deformation image to identify the location within the search window providing the best match between the points within the kernel and a corresponding kernel in the post-deformation image. Note that both the kernel and the search window are not limited to be two-dimensional. The kernel size is selected to be large enough to ensure reliable matches between corresponding points in the pre-deformation and post-deformation images, but small enough to provide for fast calculation of matching and high-resolution strain images.
The determination of a best match can be according to one of a number of different statistical techniques, for example, by computing the sum of the square of the differences between the image values of corresponding points in the kernels of the undeformed and the deformed images.
Normally the size of the search window must be great enough to accommodate likely tissue displacements between the pre-deformation and post-deformation images, but limited to manage the computational burden of matching points with each other and to reduce the chance of possible false matches that violate a priori assumptions about limited mobility of a continuum reacting to external mechanical stimuli. Additional computational speed may be provided by offsetting the location of the search window within the post-deformation data, and further limiting its size, based on previously computed displacements of nearby tissue. This approach also relies on assumptions of continuity among displacement values resulting from bounded elasticity of a known imaged material.
Commonly, when elasticity imaging is used in a medical setting, an ultrasonic transducer is used both to acquire imaging data and to provide manual deformation to the tissue. This results in an axial deformation aligned generally with the ultrasonic beam axis of the transducer in which the calculated displacement with respect to the contact of the transducer and tissue will increase with distance from the transducer.
In such systems, displacements are normally calculated on a row-by-row basis, with rows extending through the tissue generally perpendicularly to the axis of the ultrasonic beam. The computation of displacements starts at a row closest to the transducer and having lowest expected displacements, thereby limiting the necessary area of the search windows. As each row is calculated, the displacements at that row may be corrected by comparisons among row elements to remove erroneous points in light of assumptions about limits of shearing in the tissue. Once a given row is complete, the next row further away from the transducer may be computed, again using search windows sized and located using information about previously determined displacements from the previous row. When all rows are completed, an elastic strain image may be produced. Other elastic parameters such as modulus and Poison's ratio can also be estimated by the calculated displacement function.
Desirably, the time and computationally intensive matching of the kernels to data of the post-deformation data could be divided among multiple processors to be executed in parallel for improved real-time elasticity imaging. Unfortunately, each successive row of displacement data is highly dependent on the earlier rows, particularly for refining the size and location of the search windows. Further, independent processing of the rows in small groups associated with different processors raises a problem of “collisions” in which errors in the calculations of individual rows are propagated within the group to produce discontinuities when the groups meet at interfaces between the groups.
For these reasons, improvements in the execution speed of the calculation of elasticity images, highly desirable to guide the operator in manual deformation of the tissue, must wait for incremental improvements in processor speed as new processors are introduced into medical equipment.